Promises and Perils of Artificial Intelligence in Neurosurgery
Abstract Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise i...
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Published in | Neurosurgery Vol. 87; no. 1; pp. 33 - 44 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Oxford University Press
01.07.2020
Copyright by the Congress of Neurological Surgeons Wolters Kluwer Health, Inc |
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Online Access | Get full text |
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Abstract | Abstract
Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing “automation revolutions,” namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective. |
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AbstractList | Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing “automation revolutions,” namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective. Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. lndirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective. KEYWORDS: Artificial intelligence, Deep learning, Machine learning, Automation, Surgical adjuncts, Diagnostics, Prognostication Abstract Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing “automation revolutions,” namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective. Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. lndirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective. |
Audience | Academic |
Author | Parrish, Rob Fernandez-Miranda, Juan Kliot, Michel Britz, Gavin W Panesar, Sandip S Cagle, Yvonne |
AuthorAffiliation | Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas Department of Neurosurgery, Stanford University, Stanford, California Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas Department of Neurosurgery, Stanford University, Stanford, California NASA Ames Research Center, Mountain View, California Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas |
AuthorAffiliation_xml | – name: Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas Department of Neurosurgery, Stanford University, Stanford, California Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas Department of Neurosurgery, Stanford University, Stanford, California NASA Ames Research Center, Mountain View, California Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas – name: Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas Department of Neurosurgery, Stanford University, Stanford, California NASA Ames Research Center, Mountain View, California |
Author_xml | – sequence: 1 givenname: Sandip S surname: Panesar fullname: Panesar, Sandip S – sequence: 2 givenname: Michel surname: Kliot fullname: Kliot, Michel – sequence: 3 givenname: Rob surname: Parrish fullname: Parrish, Rob – sequence: 4 givenname: Juan surname: Fernandez-Miranda fullname: Fernandez-Miranda, Juan – sequence: 5 givenname: Yvonne surname: Cagle fullname: Cagle, Yvonne – sequence: 6 givenname: Gavin W surname: Britz fullname: Britz, Gavin W email: gbritz@houstonmethodist.org |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31748800$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1097/SLA.0000000000003262 10.1093/neuonc/now121 10.1158/1078-0432.CCR-17-2236 10.1001/jama.2018.17163 10.1001/jama.2017.7797 10.1016/j.clinph.2011.06.001 10.3171/2009.11.JNS09857 10.1016/j.measurement.2012.11.015 10.1093/neuonc/now086 10.1002/wsbm.1198 10.1007/s00330-016-4653-3 10.1093/neuonc/now256 10.1093/jamia/ocx125 10.1007/s00586-012-2499-1 10.1097/00006123-200011000-00008 10.1258/jhsrp.2010.010001 10.1126/scitranslmed.aad9398 10.1161/STROKEAHA.117.019740 10.5116/ijme.5b3f.9fb3 10.3174/ajnr.A1037 10.4137/BII.S38308 10.1002/jmri.21815 10.1056/NEJMra1814259 10.1038/s41551-018-0305-z 10.3171/2009.6.FOCUS09109 10.1148/radiol.2016161382 10.1016/j.nec.2014.11.011 10.1088/1741-2560/6/5/056001 10.1016/j.compmedimag.2016.03.003 10.1136/bmjqs-2018-008551 10.1016/j.acra.2008.09.015 10.1038/s41591-018-0147-y 10.1016/j.artmed.2014.03.001 10.1159/000099863 10.1007/s11548-014-1007-y 10.1177/0018720814535628 10.4103/2152-7806.142777 10.1016/j.wneu.2010.07.007 10.1148/radiol.14140770 10.1038/s41591-018-0300-7 10.1038/520609a 10.1038/s41746-018-0048-y 10.1093/neuros/nyx384 10.1097/00005373-200102000-00018 10.4103/2152-7806.95390 10.3171/2014.5.JNS132279 10.1016/j.wnsx.2019.100012 10.1097/01.sla.0000103020.19595.7d 10.1080/15376490490451552 10.1002/jmri.24696 10.1227/NEU.0b013e31827235f8 10.3171/2009.2.JNS081334 10.1136/bmj.i2139 10.1038/nature21056 10.1302/2048-0105.13.360038 10.1159/000076571 10.1038/s41551-017-0165-y 10.1148/radiol.2018180901 10.1148/radiol.2018181928 10.1097/HMR.0b013e31821826a1 10.1001/jamainternmed.2015.5231 |
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Keywords | Deep learning Surgical adjuncts Automation Prognostication Diagnostics Machine learning Artificial intelligence |
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References | Rajpurkar (bib19-20231011) 2017 Li (bib54-20231011) 2017; 1 (bib5-20231011) 2002; 31 Girão (bib55-20231011) 2013; 46 Keane (bib71-20231011) 2018; 1 Tzou (bib56-20231011) 2004; 11 Campillo-Gimenez (bib30-20231011) 2013; 192 Topol (bib1-20231011) 2019; 25 Brown (bib22-20231011) 2018; 25 Chang (bib11-20231011) 2018; 24 Rolston (bib13-20231011) 2015; 26 Dolz (bib31-20231011) 2016; 52 Yu (bib7-20231011) 2017; 27 Chen (bib39-20231011) 2013; 5 Panesar (bib10-20231011) 2019; 2 Duun-Henriksen (bib35-20231011) 2012; 123 Shortliffe (bib78-20231011) 2018; 320 Oborn (bib62-20231011) 2011; 16 Kitajima (bib33-20231011) 2009; 16 Titano (bib21-20231011) 2018; 24 Emblem (bib23-20231011) 2009; 30 Kantelhardt (bib49-20231011) 2013; 72 Cohen (bib42-20231011) 2016; 8 Enchev (bib50-20231011) 2009; 27 Senders (bib17-20231011) 2018; 83 Rajkomar (bib2-20231011) 2019; 380 Cabitza (bib66-20231011) 2017; 318 Zhou (bib24-20231011) 2017; 19 Nielsen (bib41-20231011) 2018; 49 Rotman (bib67-20231011) 2013; 16 Shademan (bib16-20231011) 2016; 8 Yu (bib18-20231011) 2018; 2 Pandya (bib51-20231011) 2009; 111 Zhang (bib27-20231011) 2017; 19 Cobb (bib61-20231011) 2012; 1 Emblem (bib43-20231011) 2015; 275 Lanfranco (bib15-20231011) 2004; 239 Sinha (bib36-20231011) 2001; 50 Rolston (bib12-20231011) 2014; 5 Dumont (bib40-20231011) 2011; 75 D’Albis (bib52-20231011) 2015; 10 Kassahun (bib25-20231011) 2014; 61 Yamashita (bib34-20231011) 2008; 29 Yu (bib59-20231011) 2019; 28 Faustinella (bib81-20231011) 2018; 9 Kickingereder (bib9-20231011) 2016; 281 Esteva (bib20-20231011) 2017; 542 Chiang (bib32-20231011) 2015; 41 Schork (bib46-20231011) 2015; 520 Ueda (bib28-20231011) 2018; 290 Ausman (bib64-20231011) 2012; 3 Panesar (bib3-20231011) 2019; 270 Rughani (bib45-20231011) 2010; 113 Ahn (bib8-20231011) 2014; 121 Nimsky (bib53-20231011) 2000; 47 Adler (bib58-20231011) 1997; 69 Casner (bib70-20231011) 2014; 56 Fereidouni (bib38-20231011) 2017; 1 Hoff (bib65-20231011) 2011; 36 Chang (bib44-20231011) 2016; 18 Tankus (bib29-20231011) 2009; 6 Bidiwala (bib26-20231011) 2004; 40 Rudie (bib37-20231011) 2019; 290 Hu (bib57-20231011) 2013; 22 Bohl (bib4-20231011) 2016; 8 Lehman (bib69-20231011) 2015; 175 Makary (bib77-20231011) 2016; 353 |
References_xml | – volume: 270 start-page: 223 issue: 2 year: 2019 ident: bib3-20231011 article-title: Artificial intelligence and the future of surgical robotics publication-title: Ann Surg doi: 10.1097/SLA.0000000000003262 contributor: fullname: Panesar – volume: 19 start-page: 109 issue: 1 year: 2017 ident: bib27-20231011 article-title: Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas publication-title: Neuro-Oncol doi: 10.1093/neuonc/now121 contributor: fullname: Zhang – volume: 24 start-page: 1073 issue: 5 year: 2018 ident: bib11-20231011 article-title: Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging publication-title: Clin Cancer Res doi: 10.1158/1078-0432.CCR-17-2236 contributor: fullname: Chang – volume: 320 start-page: 2199 issue: 21 year: 2018 ident: bib78-20231011 article-title: Clinical decision support in the era of artificial intelligence publication-title: JAMA doi: 10.1001/jama.2018.17163 contributor: fullname: Shortliffe – volume: 318 start-page: 517 issue: 6 year: 2017 ident: bib66-20231011 article-title: Unintended consequences of machine learning in medicine publication-title: JAMA doi: 10.1001/jama.2017.7797 contributor: fullname: Cabitza – volume: 123 start-page: 84 issue: 1 year: 2012 ident: bib35-20231011 article-title: Channel selection for automatic seizure detection publication-title: Clin Neurophysiol doi: 10.1016/j.clinph.2011.06.001 contributor: fullname: Duun-Henriksen – volume: 113 start-page: 585 issue: 3 year: 2010 ident: bib45-20231011 article-title: Use of an artificial neural network to predict head injury outcome publication-title: J Neurosurg doi: 10.3171/2009.11.JNS09857 contributor: fullname: Rughani – volume: 46 start-page: 1257 issue: 3 year: 2013 ident: bib55-20231011 article-title: Tactile sensors for robotic applications publication-title: Measurement doi: 10.1016/j.measurement.2012.11.015 contributor: fullname: Girão – volume: 18 start-page: 1680 issue: 12 year: 2016 ident: bib44-20231011 article-title: Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab publication-title: Neuro Oncol doi: 10.1093/neuonc/now086 contributor: fullname: Chang – volume: 5 start-page: 73 issue: 1 year: 2013 ident: bib39-20231011 article-title: Promise of personalized omics to precision medicine publication-title: Wiley Interdiscip Rev Syst Biol Med doi: 10.1002/wsbm.1198 contributor: fullname: Chen – volume: 27 start-page: 3509 issue: 8 year: 2017 ident: bib7-20231011 article-title: Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma publication-title: Eur Radiol doi: 10.1007/s00330-016-4653-3 contributor: fullname: Yu – volume: 19 start-page: 862 issue: 6 year: 2017 ident: bib24-20231011 article-title: MRI features predict survival and molecular markers in diffuse lower-grade gliomas publication-title: Neuro Oncol doi: 10.1093/neuonc/now256 contributor: fullname: Zhou – volume: 25 start-page: 568 issue: 5 year: 2018 ident: bib22-20231011 article-title: Using machine learning for sequence-level automated MRI protocol selection in neuroradiology publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocx125 contributor: fullname: Brown – volume: 22 start-page: 661 issue: 3 year: 2013 ident: bib57-20231011 article-title: Robotic-assisted pedicle screw placement: lessons learned from the first 102 patients publication-title: Eur Spine J doi: 10.1007/s00586-012-2499-1 contributor: fullname: Hu – volume: 47 start-page: 1070 issue: 5 year: 2000 ident: bib53-20231011 article-title: Quantification of, visualization of, and compensation for brain shift using intraoperative magnetic resonance imaging publication-title: Neurosurgery doi: 10.1097/00006123-200011000-00008 contributor: fullname: Nimsky – volume: 16 start-page: 46 issue: 1 year: 2011 ident: bib62-20231011 article-title: Robots and service innovation in health care publication-title: J Health Serv Res Policy doi: 10.1258/jhsrp.2010.010001 contributor: fullname: Oborn – volume: 8 issue: 337 year: 2016 ident: bib16-20231011 article-title: Supervised autonomous robotic soft tissue surgery publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aad9398 contributor: fullname: Shademan – volume: 49 start-page: 1394 issue: 6 year: 2018 ident: bib41-20231011 article-title: Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning publication-title: Stroke doi: 10.1161/STROKEAHA.117.019740 contributor: fullname: Nielsen – volume: 9 start-page: 195 year: 2018 ident: bib81-20231011 article-title: The decline of clinical skills: a challenge for medical schools publication-title: Int J Med Educ doi: 10.5116/ijme.5b3f.9fb3 contributor: fullname: Faustinella – volume: 1 issue: 1 year: 2017 ident: bib54-20231011 article-title: Computer-assisted neurosurgery: Yesterday, today and tomorrow publication-title: Neurosurg J contributor: fullname: Li – volume: 29 start-page: 1153 issue: 6 year: 2008 ident: bib34-20231011 article-title: Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A1037 contributor: fullname: Yamashita – volume: 8 start-page: 11 year: 2016 ident: bib42-20231011 article-title: Methodological issues in predicting pediatric epilepsy surgery candidates through natural language processing and machine learning publication-title: Biomed Inform Insights doi: 10.4137/BII.S38308 contributor: fullname: Cohen – volume: 30 start-page: 1 issue: 1 year: 2009 ident: bib23-20231011 article-title: Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge-based fuzzy clustering publication-title: J Magn Reson Imaging doi: 10.1002/jmri.21815 contributor: fullname: Emblem – volume: 380 start-page: 1347 issue: 14 year: 2019 ident: bib2-20231011 article-title: Machine learning in medicine publication-title: N Engl J Med doi: 10.1056/NEJMra1814259 contributor: fullname: Rajkomar – volume: 2 start-page: 719 issue: 10 year: 2018 ident: bib18-20231011 article-title: Artificial intelligence in healthcare publication-title: Nat Biomed Eng doi: 10.1038/s41551-018-0305-z contributor: fullname: Yu – volume: 27 start-page: E11 issue: 3 year: 2009 ident: bib50-20231011 article-title: Neuronavigation: geneology, reality, and prospects publication-title: Neurosurg Focus doi: 10.3171/2009.6.FOCUS09109 contributor: fullname: Enchev – volume: 281 start-page: 907 issue: 3 year: 2016 ident: bib9-20231011 article-title: Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features publication-title: Radiology doi: 10.1148/radiol.2016161382 contributor: fullname: Kickingereder – volume: 26 start-page: 149 issue: 2 year: 2015 ident: bib13-20231011 article-title: Errors in neurosurgery publication-title: Neurosurg Clin N Am doi: 10.1016/j.nec.2014.11.011 contributor: fullname: Rolston – volume: 6 start-page: 056001 issue: 5 year: 2009 ident: bib29-20231011 article-title: An automatic measure for classifying clusters of suspected spikes into single cells versus multiunits publication-title: J Neural Eng doi: 10.1088/1741-2560/6/5/056001 contributor: fullname: Tankus – volume: 52 start-page: 8 year: 2016 ident: bib31-20231011 article-title: Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study publication-title: Comput Med Imaging Graph doi: 10.1016/j.compmedimag.2016.03.003 contributor: fullname: Dolz – volume: 28 start-page: 238 issue: 3 year: 2019 ident: bib59-20231011 article-title: Framing the challenges of artificial intelligence in medicine publication-title: BMJ Qual Saf doi: 10.1136/bmjqs-2018-008551 contributor: fullname: Yu – volume: 16 start-page: 313 issue: 3 year: 2009 ident: bib33-20231011 article-title: Differentiation of common large sellar-suprasellar masses: effect of artificial neural network on radiologists’ diagnosis performance publication-title: Acad Radiol doi: 10.1016/j.acra.2008.09.015 contributor: fullname: Kitajima – volume: 24 start-page: 1337 issue: 9 year: 2018 ident: bib21-20231011 article-title: Automated deep-neural-network surveillance of cranial images for acute neurologic events publication-title: Nat Med doi: 10.1038/s41591-018-0147-y contributor: fullname: Titano – volume: 61 start-page: 79 issue: 2 year: 2014 ident: bib25-20231011 article-title: Automatic classification of epilepsy types using ontology-based and genetics-based machine learning publication-title: Artif Intell Med doi: 10.1016/j.artmed.2014.03.001 contributor: fullname: Kassahun – volume: 69 start-page: 124 issue: 1-4 year: 1997 ident: bib58-20231011 article-title: The Cyberknife: a frameless robotic system for radiosurgery publication-title: Stereotact Funct Neurosurg doi: 10.1159/000099863 contributor: fullname: Adler – volume: 16 start-page: 28 issue: 4 year: 2013 ident: bib67-20231011 article-title: How technology is destroying jobs publication-title: Technol Rev contributor: fullname: Rotman – volume: 10 start-page: 117 issue: 2 year: 2015 ident: bib52-20231011 article-title: PyDBS: an automated image processing workflow for deep brain stimulation surgery publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-014-1007-y contributor: fullname: D’Albis – volume: 56 start-page: 1506 issue: 8 year: 2014 ident: bib70-20231011 article-title: The retention of manual flying skills in the automated cockpit publication-title: Hum Factors doi: 10.1177/0018720814535628 contributor: fullname: Casner – volume: 5 start-page: S435 issue: suppl 10 year: 2014 ident: bib12-20231011 article-title: Medical errors in neurosurgery publication-title: Surg Neurol Int doi: 10.4103/2152-7806.142777 contributor: fullname: Rolston – volume: 75 start-page: 57 issue: 1 year: 2011 ident: bib40-20231011 article-title: Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models publication-title: World Neurosurg doi: 10.1016/j.wneu.2010.07.007 contributor: fullname: Dumont – volume: 275 start-page: 228 issue: 1 year: 2015 ident: bib43-20231011 article-title: A generic support vector machine model for preoperative glioma survival associations publication-title: Radiology doi: 10.1148/radiol.14140770 contributor: fullname: Emblem – volume: 25 start-page: 44 issue: 1 year: 2019 ident: bib1-20231011 article-title: High-performance medicine: the convergence of human and artificial intelligence publication-title: Nat Med doi: 10.1038/s41591-018-0300-7 contributor: fullname: Topol – volume: 520 start-page: 609 issue: 7549 year: 2015 ident: bib46-20231011 article-title: Personalized medicine: time for one-person trials publication-title: Nat News doi: 10.1038/520609a contributor: fullname: Schork – volume: 1 start-page: 40 issue: 1 year: 2018 ident: bib71-20231011 article-title: With an eye to AI and autonomous diagnosis publication-title: NPJ Digit Med doi: 10.1038/s41746-018-0048-y contributor: fullname: Keane – volume: 192 start-page: 572 year: 2013 ident: bib30-20231011 article-title: Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France publication-title: Stud Health Technol Inform contributor: fullname: Campillo-Gimenez – volume: 83 start-page: 181 issue: 2 year: 2018 ident: bib17-20231011 article-title: Natural and artificial intelligence in neurosurgery: a systematic review publication-title: Neurosurgery doi: 10.1093/neuros/nyx384 contributor: fullname: Senders – volume: 50 start-page: 308 issue: 2 year: 2001 ident: bib36-20231011 article-title: Artificial neural network predicts CT scan abnormalities in pediatric patients with closed head injury publication-title: J Trauma doi: 10.1097/00005373-200102000-00018 contributor: fullname: Sinha – volume: 3 year: 2012 ident: bib64-20231011 article-title: The transition of neurosurgeons through the technology and information age publication-title: Surg Neurol Int doi: 10.4103/2152-7806.95390 contributor: fullname: Ausman – volume: 121 start-page: 367 issue: 2 year: 2014 ident: bib8-20231011 article-title: Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging publication-title: J Neurosurg doi: 10.3171/2014.5.JNS132279 contributor: fullname: Ahn – volume: 2 start-page: 100012 year: 2019 ident: bib10-20231011 article-title: Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database publication-title: World Neurosurg X doi: 10.1016/j.wnsx.2019.100012 contributor: fullname: Panesar – volume: 239 start-page: 14 issue: 1 year: 2004 ident: bib15-20231011 article-title: Robotic surgery publication-title: Ann Surg doi: 10.1097/01.sla.0000103020.19595.7d contributor: fullname: Lanfranco – volume: 11 start-page: 367 issue: 4-5 year: 2004 ident: bib56-20231011 article-title: Smart materials, precision sensors/actuators, smart structures, and structronic systems publication-title: Mech Adv Mater Struct doi: 10.1080/15376490490451552 contributor: fullname: Tzou – volume: 41 start-page: 1689 issue: 6 year: 2015 ident: bib32-20231011 article-title: Computer-automated focus lateralization of temporal lobe epilepsy using fMRI publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24696 contributor: fullname: Chiang – volume: 72 start-page: A19 issue: suppl_1 year: 2013 ident: bib49-20231011 article-title: Evaluation of a completely robotized neurosurgical operating microscope publication-title: Neurosurgery doi: 10.1227/NEU.0b013e31827235f8 contributor: fullname: Kantelhardt – volume: 111 start-page: 1141 issue: 6 year: 2009 ident: bib51-20231011 article-title: Advancing neurosurgery with image-guided robotics: technical note publication-title: J Neurosurg doi: 10.3171/2009.2.JNS081334 contributor: fullname: Pandya – volume: 353 start-page: i2139 year: 2016 ident: bib77-20231011 article-title: Medical error—the third leading cause of death in the US publication-title: BMJ doi: 10.1136/bmj.i2139 contributor: fullname: Makary – volume: 31 start-page: 256 issue: 7 year: 2002 ident: bib5-20231011 article-title: Evaluation of the computer motion AESOP 3000 robotic endoscope holder publication-title: Health Devices – volume: 542 start-page: 115 issue: 7639 year: 2017 ident: bib20-20231011 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature doi: 10.1038/nature21056 contributor: fullname: Esteva – volume: 1 start-page: 2 issue: 3 year: 2012 ident: bib61-20231011 article-title: Are robots taking over orthopaedic surgery publication-title: Bone Jt 360 doi: 10.1302/2048-0105.13.360038 contributor: fullname: Cobb – volume: 8 start-page: e662 issue: 6 year: 2016 ident: bib4-20231011 article-title: A prospective cohort evaluation of a robotic, auto-navigating operating microscope publication-title: Cureus contributor: fullname: Bohl – volume: 40 start-page: 8 issue: 1 year: 2004 ident: bib26-20231011 article-title: Neural network classification of pediatric posterior fossa tumors using clinical and imaging data publication-title: Pediatr Neurosurg doi: 10.1159/000076571 contributor: fullname: Bidiwala – volume: 1 start-page: 957 issue: 12 year: 2017 ident: bib38-20231011 article-title: Microscopy with ultraviolet surface excitation for rapid slide-free histology publication-title: Nat Biomed Eng doi: 10.1038/s41551-017-0165-y contributor: fullname: Fereidouni – volume: 290 start-page: 187 issue: 1 year: 2018 ident: bib28-20231011 article-title: Deep learning for MR angiography: automated detection of cerebral aneurysms publication-title: Radiology doi: 10.1148/radiol.2018180901 contributor: fullname: Ueda – volume: 290 start-page: 607 issue: 3 year: 2019 ident: bib37-20231011 article-title: Emerging applications of artificial intelligence in neuro-oncology publication-title: Radiology doi: 10.1148/radiol.2018181928 contributor: fullname: Rudie – year: 2017 ident: bib19-20231011 article-title: CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning publication-title: ArXiv171105225 contributor: fullname: Rajpurkar – volume: 36 start-page: 338 issue: 4 year: 2011 ident: bib65-20231011 article-title: Deskilling and adaptation among primary care physicians using two work innovations publication-title: Health Care Manage Rev doi: 10.1097/HMR.0b013e31821826a1 contributor: fullname: Hoff – volume: 175 start-page: 1828 issue: 11 year: 2015 ident: bib69-20231011 article-title: Diagnostic accuracy of digital screening mammography with and without computer-aided detection publication-title: JAMA Intern Med doi: 10.1001/jamainternmed.2015.5231 contributor: fullname: Lehman |
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Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit... Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid... |
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SubjectTerms | Algorithms Artificial intelligence Artificial Intelligence - trends Automation Computer-aided medical diagnosis Forecasts and trends Humans Machine learning Medical diagnosis Medical prognosis Methods Nervous system Neurosurgery Neurosurgery - methods Neurosurgery - trends Neurosurgical Procedures - methods Neurosurgical Procedures - trends Surgery |
Title | Promises and Perils of Artificial Intelligence in Neurosurgery |
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